The aim of this investigation was to prepare, characterize and optimize the aceclofenac proniosomes using central composite design and carry out stability studies. Three independent variables selected were molar ratio of drug to lipid (X1), surfactant loading (X2) and volume of hydration (X3). Based on central composite design, 16 batches of proniosomes were prepared by slurry method and evaluated for the percentage drug entrapment (PDE) and mean volume diameter (MVD). The PDE and MVD (dependent variables) and the transformed values of independent variables were subjected to multiple regressions to establish a second order polynomial equation. Contour plots were constructed to further elucidate the relationship between the independent and dependent variables. The conformity of the polynomial equations was checked by preparing three checkpoint batches. From the computer optimization process and contour plots, predicted levels of independent variables X1, X2, and X3 (-0.77, -0.8 and 0 respectively), for an optimum response of PDE with constraints on MVD were determined. The optimized batch was subjected to stability studies. The polynomial equations and contour plots developed using central composite design allowed us to prepare proniosomes with optimum responses. Proniosomes stored refrigerated and at room temperature, were both found to be stable.

The aim of this investigation was to prepare, characterize and optimize the
aceclofenac proniosomes using central composite design and carry out stability
studies. Three independent variables selected were molar ratio of drug to lipid
(X1), surfactant loading (X2) and volume of hydration (X3). Based on central
composite design, 16 batches of proniosomes were prepared by slurry method and
evaluated for the percentage drug entrapment (PDE) and mean volume diameter (MVD).
The PDE and MVD (dependent variables) and the transformed values of independent
variables were subjected to multiple regressions to establish a second order
polynomial equation. Contour plots were constructed to further elucidate the
relationship between the independent and dependent variables. The conformity of
the polynomial equations was checked by preparing three checkpoint batches. From
the computer optimization process and contour plots, predicted levels of
independent variables X1, X2, and X3 (-0.77, -0.8 and 0 respectively), for an
optimum response of PDE with constraints on MVD were determined. The optimized
batch was subjected to stability studies. The polynomial equations and contour
plots developed using central composite design allowed us to prepare proniosomes
with optimum responses. Proniosomes stored refrigerated and at room temperature,
were both found to be stable.

Many drugs, those currently available in the market and those under development,
have poor aqueous solubilities that result in variable bioavailabilities. This
problem can be overcome by entrapping the drug into niosomes. Niosomes are
non-ionic surfactant vesicles that can entrap a solute in a manner analogous to
liposomes. They are osmotically active, and are stable on their own, while also
increasing the stability of the entrapped drugs (1, 2). Handling and storage of
surfactants require no special conditions. Niosomes possess an infrastructure
consisting of hydrophilic and hydrophobic moieties together, and as a result,
can accommodate drug molecules with a wide range of solubilities (3). Although
niosomes as drug carriers have shown advantages such as being cheap and
chemically stable, they are associated with problems related to physical
stability such as fusion, aggregation, sedimentation and leakage on storage. All
methods traditionally used for preparation of niosomes are time consuming and
many involve specialized equipments. Most of these methods allow only for a
predetermined lot size so material is often wasted if smaller quantities are
required for particular dose application (4).

The proniosome approach minimizes these problems as it is a dry and free flowing
product which is more stable during sterilization and storage. Ease of transfer,
distribution, measuring and storage make it a versatile delivery system.
Proniosomes are water-soluble carrier particles coated with surfactant, which
can be measured out as needed and hydrated to form niosomes immediately before
use on brief agitation in hot aqueous media (4-6).

In the present study the slurry method was used for the preparation and
optimization study of aceclofenac, as this method is simple and easy to scale
up. Aceclofenac is a poorly water soluble, non-steroidal anti-inflammatory drug
which acts specifically on inflammatory sites and thereby decreases the
inflammation. It is highly effective as an anti-inflammatory drug for various
inflammatory conditions like rheumatoid arthritis, osteoarthritis and ankylosing
spondylitis (7).

Apart from surfactant loading, other formulation variables like molar ratio of
drug to lipid and volume of hydration at the time of reconstitution also affect
the characteristics of proniosome-derived niosomes. The proniosomes are thus
needed to be optimized for the desired response. Many statistical experimental
designs have been recognized as useful techniques to optimize the formulation
and process variables (8). Different types of experimental design include
3-level factorial design (9), D-optimal design (10), and Central composite
design (11). Central composite design requires fewer runs in a 3 factor
experimental design and hence was selected for the present study.

The aim of the present study was to prepare, characterize and optimize the
aceclofenac proniosomes for percentage drug entrapment (PDE) with constraints on
the mean volume diameter (MVD) using central composite design and to carry out
stability studies on them. The independent variables selected for the present
study are molar ratio of drug to lipid (X1), surfactant loading (X2) and volume
of hydration (X3). The dependent variables included are PDE and MVD of
proniosome-derived niosomes.

Experimental

Materials

Aceclofenac was a gift from Alembic ltd. (Vadodara, India). Span 60 and
cholesterol were purchased from S.D. Fine Chemicals (Mumbai, India). Dialysis
tube (DM-70; Capacity: 2.41ml/cm, width: 29.31 mm, Avg. diameter 17.5 mm and
molecular weight cut off: 12000 to 14000) was purchased from Hi-Media
Laboratories (Mumbai, India). Chloroform, disodium hydrogen phosphate, potassium
dihydrogen phosphate and sodium chloride were procured from National Chemicals.
(Vadodara, India). All chemicals used in the study were of analytical grade and
used without further purification.

Method

Central composite experimental design

Traditionally pharmaceutical formulations are developed by changing one variable
at a time. By this method it is difficult to develop an optimized formulation,
as it does not give an idea about the interactions among the variables. Hence, a
central composite experimental design with 3 factors, 3 levels and 16 runs was
selected for the optimization study. This design consists of 8 full factorial
design points, 6 axial points, and 2 center points.

Independent variables with their levels and the dependent variables selected are
listed in Table 1. The second order polynomial equation generated from this
experimental design using Microsoft Excel is described as:

Where Yi is the dependent variable while b0 is the intercept; b1 to b33 are the
regression coefficients; and X1, X2 and X3 are the independent variables (12)
levels of which were selected from the preliminary experiments.

Preparation of proniosomes

Proniosomes were prepared by the slurry method (4). For the ease of preparation
250 mmol stock solutions of span 60 and cholesterol in chloroform were prepared.
All the batches were prepared according to the experimental design given in
Table 2. The required volume of span 60 and cholesterol stock solution per g of
carrier, and the drug dissolved in chloroform were added to a 100 ml round
bottom flask containing the maltodextrin as a carrier. Additional chloroform was
added to form a slurry in in stances of lower surfactant loading. The flask was
attached to a rotary flash evaporator (EIE-R, India.) to evaporate chloroform at
at the speed of 60-70 rpm, temperature of 45?2?C and under vacuum (600 mmHg)
until the mass in the flask resulted in a dry free flowing product. These
proniosomes were used for preparation of niosomes and characterization of the
surface characteristics by scanning electron microscopy.

Proniosomes were transformed to niosomes by hydrating with phosphate buffer
saline (PBS) with a pH of 7.4 at 80?C using vortex mixer for 2 min. The niosomes
were sonicated twice for 30 s using a 250-W probe-type sonicator (MAGNA-PAK-250,
Libra Ultrasonic, India). Niosomes were prepared in such a manner that total
surfactant concentration remained at 10 mmol in all the batches. Niosomes were
characterized for morphology, PDE and vesicle size in terms of MVD.

Scanning electron microscopy

Proniosomes were sprinkled on to the double-sided tape that was affixed on
aluminum stubs. The aluminum stub was placed in the vacuum chamber of a scanning
electron microscope (XL 30 ESEM with EDAX, Philips, Netherlands). The samples
were observed for morphological characterization using a gaseous secondary
electron detector (working pressure: 0.8 torr, acceleration voltage: 30.00 KV)
XL 30, (Philips, Netherlands).

Optical microscopy

The hydrated niosome dispersions prepared from proniosomes were observed using
optical microscopy. After suitable dilution, the noisome dispersions on glass
slide and viewed by a microscope (Medilux-207R (II), Kyowa-Getner, India) with a
magnification of 1200X.

Percentage drug entrapment (PDE)

The entrapped aceclofenac within niosomes was determined after removing the
unentrapped drug by dialysis (13). The dialysis was carried out by taking
niosomal dispersion in dialysis tube (donor compartment), which was dipped in a
beaker containing 400 ml of PBS with a pH of 7.4 (receptor compartment). The
beaker was placed on a magnetic stirrer run for 4 h with a speed of 80-120 rpm.
Then, the solution inside the receptor compartment was studied for unentrapped
aceclofenac at 275 nm using an UV spectrophotometer (UV 1601, Shimadzu, Japan).
The PDE in the niosomes was calculated from the ratio of the difference of the
total amount of drug added and the amount of unentrapped drug detected, to the
total amount of drug added.

Measurement of vesicle size

The vesicle dispersions were diluted about 100 times in the same buffer used for
their preparation. Vesicle size was measured on a particle size analyzer (Laser
diffraction particle size analyzer, Sympatec, Germany). The apparatus consists
of a He-Ne laser beam of 632.8 nm focused with a minimum power of 5 mW using a
fourier lens [R-5] to a point at the center of multielement detector and a small
volume sample holding cell (Su cell). The sample was stirred using a stirrer
before determining the vesicle size.

Stability studies

To determine the stability of proniosomes,the optimized batch was stored in
airtight sealed vials at 2-8?C temperature and room temperature (R. T.). Surface
characteristics and percentage drug retained in proniosomes and
proniosome-derived niosomes were selected as parameters for evaluation of the
stability, since instability of the formulation would reflect in drug leakage
and a decrease in the percentage drug retained. The proniosomes were sampled at
regular intervals of time (0, 1, 2, and 3 months), observed for color change and
surface characteristics, and tested for the percentage drug retained after being
hydrated to form niosomes. The percentage drug retained was determined from the
ratio of the entrapment to the initial entrapment of the drug.

Results and Discussion

Morphology of dry proniosomes and proniosome-derived niosomes

Proniosomes were prepared by the slurry method using maltodextrin as a carrier.
Scanning electron microscopy (SEM) of uncoated maltodextrin powder (Figure 1a)
shows the highly porous surface, which would provide more surface area to be
coated with surfactant mixture. Proniosomes were made with different proportions
of drug and surfactant coating. Figure 1b, c and d are SEM images of different
proniosome batches made at different surfactant loading. Surface of the
proniosomes batches PA2 and PA15, made at 1.5X and 3X respectively, was observed
as being smooth and uniform while that of batch PA8, made at 4.5X surfactant
loading was seen rough, thick and uneven. Morphology of proniosome-derived
niosomes were studied under optical microscope. Niosomes prepared from
proniosomes were spherical in shape (Figure 2).

Optimization study of proniosomes

An optimization using central composite design for 3 factors, 3 levels offers an
advantage of fewer experimental runs (16 runs) as compared with that of 3
factors, 3 levels full factorial design, which requires 27 runs. The
experimental runs and the observed responses for the 16 batches are given in
Table 2. The different levels of independent variable combinations resulted in
different PDE and MVD values. The PDE values observed were in the range of
56.76% in batch PA7 (minimum) to 74.86% in batch PA9 (maximum). This indicates
selected three independent variables have a profound effect on the PDE within
proniosome-derived niosomes. The second order polynomial equation relating the
response PDE and the independent variables was:

The values of the coefficients X1-X3 are related to the effect of these
variables on the PDE. Coefficients of more than one terms represents interaction
and show how the response changes when two factors are simultaneously changed.
Coefficients of higher order terms represent quadratic relationship and are
included to investigate nonlinearity. The polynomial equation can be used to
draw conclusions after considering the magnitude of each coefficient and the
mathematical sign it carries (i.e., positive or negative). The high value (0.98)
of correlation coefficient (R2) for Equation 2 indicates a good fit. Proniosomal
batches PA1, PA2, PA3, PA4, PA9 and PA14 exhibited high PDE value, i.e.more than
70% (Table 2). A negative sign of coefficient for molar ratio of drug: lipid
(X1) and surfactant loading (X2) represents antagonistic effect of these
variables. In this study at different levels of X1, lipid was kept constant and
the amount of drug was increased for each level to give a different molar ratio.
So at a low level of X1 high PDE value might be due to more availability of
lipophilic ambience for the drug entrapment. A positive sign of the coefficient
for volume of hydration (X3) represents a favourable effect. This may be due to
efficient hydration that takes place at a high level of X3 during transformation
of proniosomes to niosomes, resulting in a high PDE within niosomes. The
significance of the different formulation variables and their interactions was
compared using analysis of variance (ANOVA) at a significance level of p<0.05.
From the P value for PDF analysis given in Table 3, it can be concluded that the
molar ratio of drug: lipid and volume of hydration have significant effects on
the PDE of aceclofenac proniosome-derived niosomes and no interaction term has a
significant effect on the PDE.

Vesicle size (MVD) of the niosome batches was measured by low angle laser light
scattering technique and was found to be in the range of 3.46 ?m to 8.4 ?m. A
polynomial equation was developed for MVD, described as:

The value of the correlation coefficient (R2) of Equation 3 was found to be
0.95, indicating a good fit. A positive sign of the coefficients for the molar
ratio of drug: lipid and surfactant loading indicates favorable effects on MVD.
Positive effects of X1 could be attributed to hydrophobic interaction between
drug and surfactant. Favourable effect of X2 may be due to efficient hydration
of the uniform and thin film of surfactant at low surfactant loading compared to
the film obtained at a high surfactant loading.

Negative sign of the coefficient for the volume of hydration (X3) indicates a
negative effect. As shown in Table 4, among the independent variables selected
the terms X1 and X2 were found to be significant (P<0.05) in predicting the MVD.
It is also evident from Table 4 that the quadratic effects of all the
independent variables i.e. X12, X22 and X32 have significant effects on MVD.

As the central composite design includes two center points, we can estimate the
pure error of the experiments and enable the model?s to be checked for lack of
fit. For the experimentally obtained data, the test for lack of fit did not
yield statistical significance (P>0.05), and the results indicated that the
models for PDE and MVD were satisfactory (Table 3 and 4).

Contour plots

Presentation of the data as graphs can help to show the relationship between the
independent and dependent variables. First contour plot was constructed at
medium level of X2, as this term is not significant in predicting the PDE value
(Table 3). The effects of X1 and X3 with their interaction on PDE and MVD at a
fixed level of X2 (medium level) are shown in Figure 3. The plots for PDE were
found to be linear which indicates a linear relationship between X1 and X3. It
was determined from the contour plot that high values of PDE (≥70%) could be
obtained with different combinations of an X1 value below -0.73 level and X3
values in the entire range from -1 level to 1 level. It is evident from the
contour that the low level of X1 favurs high PDE value of proniosome-derived
niosomes. Lipid was present in high proportion at low level of X1, which can
accommodate more drug, as where at a high level of X1 (as the drug is present in
a higher amount compared to a low level) saturation of lipid domains with
reference to drug provides limited PDE value (14). Furthermore, Figure 3 also
indicates low values of MVD can be obtained with low level of X1 and high level
of X3. Coefficient value for the term X3 in equation 2 (b3=1.59) indicates
positive effect on the PDE of proniosome-derived niosomes but at a high level of
X3 dilution of the niosomal dispersion takes place. Hence, another contour plot
was constructed at medium level of the X3.

Figure 4 is a contour plot drawn at 0 level of X3, showing the effect of X1 and
X2 on MVD and PDE of proniosome-derived niosomes. The contours for all the
values of MVD were found to be nonlinear. It was evident from Figure 4 that low
value of MVD could be obtained with low level of both X1 and X2 and that high
values of PDE (≥72%) can be obtained for different combinations of the two
independent variables, X1 in the range of less than -0.8 level and X2 in the
entire range of -1 level to 1 level.

Checkpoint analysis

Three checkpoint batches were prepared for different combinations of independent
variables and evaluated for PDE and MVD. The results shown in Table 5 indicate
that the measured PDE and MVD values were as expected from the theoretical
values computed from the polynomial equations and contour plots. When compared
with the predicted PDE and MVD using student t-test the differences were found
to be insignificant (P> 0.05). Thus, we can conclude that the obtained
mathematical equations and contour plots are valid for predicting the value of
PDE and MVD.

Optimum formula

After studying the effects of the independent variables on the responses, the
levels of these variables that give the optimum responses were determined.
Volume of hydration is a critical factor for preparation of niosomes from
proniosomes as inadequate volume of hydration results in improper hydration of
the film. Although, high values of PDE could be obtained with the entire range
of volume of hydration (X3), and it affects the final concentration of the lipid
in niosomal dispersions. Hence, medium level of X3 was selected as an optimum
for the aceclofenac proniosomes.

The optimum formulation is one that gives a high value of PDE (≥70%) and is
constrained to a low MVD (≤ 5 ?m) as well as having a high total amount of drug
entrapped and low amount of carrier present in the resultant niosomes. Using a
computer optimization process and the contour plots shown in Figure 4, the
levels selected for both X1 and X2 were -0.77 and -0.8 respectively, which gives
the theoretical value of 71.84% and 4.99 ?m for PDE and MVD, respectively.

Decreasing the level of X2 from the optimum level resulted in a significant
increase in the amount of carrier but an insignificant increase in the PDE
value. However, an increase in the level of X1 above the selected level led to
an increase in the PDE value but as well an increase in the vesicle size above
the desired value. Hence, -0.77 level of molar ratio of drug: lipid (X1), -0.8
level of surfactant loading (X2) and 0 level of volume of hydration (X3) were
selected as optimum. For a confirmation, a fresh formulation (Batch PAO) was
prepared at the optimum levels of the independent variables and the resultant
proniosomes were transformed to niosomes and evaluated for the responses. The
observed values of PDE and MVD were found to be 70.28% and 5.12 ?m respectively,
which were in close agreement with the theoretical values.

Stability studies

It was observed that there was no change in color of the proniosomes up to 3
months of storage. Figure 5 shows SEM images of the PAO batch at initial time
and after 3 months at both storage conditions. It is evident from Figure 5a, b
and c that surface characteristics of the proniosomes did not alter. The results
of the percentage of drug retained are depicted in Table 6. Proniosomes of batch
PAO were found to be stable and showed no significant difference in percentage
of drug retained at both storage temperatures.

Conclusion

The slurry method was found to be simple and suitable for laboratory scale
preparation of aceclofenac proniosomes. The statistical approach for
optimization of formulation is a useful tool, when several variables are to be
studied simultaneously. The polynomial equations and contour plots developed by
using central composite design allowed us to prepare proniosomes with optimum
characteristics. Proniosomes, stored at refrigerated and room temperature were
both found to be stable.